How I work
The AI failures most teams hit share one cause: someone jumped to building before figuring out what was worth building, or whether it would actually work. My process is designed to prevent exactly that. Fast to clarity, with the right people on board, ending in something real.
Dig in fast (days, not weeks)
I get into how your business actually works: the data, the systems, the goal. We find the use cases that are genuinely high-ROI and genuinely buildable, and kill the ones that aren't before they cost anyone months or millions.
Get buy-in
The best build dies in committee if the right people aren't aligned. I make the case to stakeholders in their language, with honest tradeoffs and a low-risk first step, so cautious teams can say yes instead of defaulting to no.
Build it
Then I actually build. Code, integrations, evals, guardrails. A working system running in your stack, proven against your real data before it touches anything that matters.
Hand off
Your team owns it. You get the evals, docs, and the actual understanding to run and extend it. No black box, no lock-in, no permanent dependency on me.
What I hold to
- Prove it small before you spend big. A cheap, evaluated prototype beats a year-long bet.
- Evals first. Nothing ships without a way to know it's actually working.
- Honest about tradeoffs. If AI isn't the right answer, I'll say so.
- Safety where it counts. Extra rigor the moment an agent touches money or prod.
- Your team owns it. I transfer understanding, not lock-in.
Ways to work together
- Diagnostic sprint: a fast, paid dig-in that ends with a prioritized, costed plan of what's worth building (and what isn't).
- Build engagement: I build a specific system or workflow end-to-end and hand it to your team.
- Embedded / fractional: your part-time AI engineer for a stretch, shipping alongside your team.